Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Towards language independent automated learning of text categorization models
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
Fast discovery of association rules
Advances in knowledge discovery and data mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
ANLC '94 Proceedings of the fourth conference on Applied natural language processing
Concept-based knowledge discovery in texts extracted from the Web
ACM SIGKDD Explorations Newsletter
A domain independent environment for creating information extraction modules
Proceedings of the tenth international conference on Information and knowledge management
Inductive Learning of a Knowledge Dictionary for a Text Mining System
Proceedings of the 14th International conference on Industrial and engineering applications of artificial intelligence and expert systems: engineering of intelligent systems
Domain knowledge to support the discovery process: constraints
Handbook of data mining and knowledge discovery
Case studies: Commercial domain, single mining tasks systems: document explorer
Handbook of data mining and knowledge discovery
Handbook of data mining and knowledge discovery
Text mining: generating hypotheses from MEDLINE
Journal of the American Society for Information Science and Technology
TopCat: Data Mining for Topic Identification in a Text Corpus
IEEE Transactions on Knowledge and Data Engineering
Integrating information retrieval and data mining to discover project team coordination patterns
Decision Support Systems
Proceedings of the 2013 workshop on Automated knowledge base construction
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This paper describes the FACT system for knowledge discovery fromtext. It discovers associations—patterns ofco-occurrence—amongst keywords labeling the items in a collection oftextual documents. In addition, when background knowledge is available aboutthe keywords labeling the documents FACT is able to use this information inits discovery process. FACT takes a query-centered view of knowledgediscovery, in which a discovery request is viewed as a query over theimplicit set of possible results supported by a collection of documents, andwhere background knowledge is used to specify constraints on the desiredresults of this query process. Execution of a knowledge-discovery query isstructured so that these background-knowledge constraints can be exploitedin the search for possible results. Finally, rather than requiring a user tospecify an explicit query expression in the knowledge-discovery querylanguage, FACT presents the user with a simple-to-use graphical interface tothe query language, with the language providing a well-defined semantics forthe discovery actions performed by a user through the interface.